@inproceedings{yu-etal-2017-syllable,
    title = "Syllable-level Neural Language Model for Agglutinative Language",
    author = "Yu, Seunghak  and
      Kulkarni, Nilesh  and
      Lee, Haejun  and
      Kim, Jihie",
    editor = "Faruqui, Manaal  and
      Schuetze, Hinrich  and
      Trancoso, Isabel  and
      Yaghoobzadeh, Yadollah",
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W17-4113/",
    doi = "10.18653/v1/W17-4113",
    pages = "92--96",
    abstract = "We introduce a novel method to diminish the problem of out of vocabulary words by introducing an embedding method which leverages the agglutinative property of language. We propose additional embedding derived from syllables and morphemes for the words to improve the performance of language model. We apply the above method to input prediction tasks and achieve state of the art performance in terms of Key Stroke Saving (KSS) w.r.t. to existing device input prediction methods."
}